The relationship between nut consumption and premature coronary artery disease in a representative sample of Iranians: Iran-premature coronary artery disease (IPAD) study
Bibliographic record
Abstract
Abstract Objective: The cardioprotective effects of nuts are well established. However, the positive impacts of nuts in preventing CVD at a younger age, a condition known as premature coronary artery disease (PCAD), is still debated. Therefore, we aim to determine the association between nuts and PCAD occurrence and its severity in different Iranian ethnicities. Design: This case–control study was conducted within the framework of the Iran-premature coronary artery disease (I-PAD) study, an ongoing multi-centric study on Iranian patients of different ethnicities. Setting: This multi-centric case–control study was conducted in among 3253 persons under the age of 70 years in women and 60 years in men from different ethnicities in Iran. Participants: Information on nut consumption was collected using a validated FFQ. Subjects were selected from among the candidates for angiography. Cases were those whose coronary angiography showed stenosis of more than 75 % in at least one vessel or more than 50 % of the left main artery, while the control group participants had normal angiography results. Results: In the crude model, compared to the first quartile, the highest quartile of nut consumption was significantly associated with a lower risk of PCAD (OR = 0·26, 95 % CI (0·21, 0·32); Pfor trend = 0·001). In the top quartile of nut intake, a substantial decrease in PCAD was observed after controlling for putative confounders (OR = 0·32; 95 % CI (0·24, 0·43); Pfor trend = 0·001). Additionally, a 75 % decrease in the risk of severe PCAD was observed in the participants in the highest quartile of nut intake. Conclusion: A significant inverse association was observed between nut intake and the risk and severity of PCAD in the Iranian population. Large-scale clinical trials are required to confirm these findings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".